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Roland Piquepaille writes "Networks are used to represent the structure of complex systems, including the Internet or social networks, but often these descriptions are biased or incomplete. Now, researchers at the Santa Fe Institute (SFI) have shown that it's possible to extract automatically the hierarchical structure of networks. The researchers say their results 'suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.' They also think that their algorithms can be applied to almost every kind of networks, from biochemical networks (protein interaction networks, metabolic networks or genetic regulatory networks) to communities in social networks. But read more for additional references and some pictures about hierarchical networks and their applications."

"Yech this stuff is so silly. [snip] I am shocked that such vague nonsense of this is in the journal Nature."

The thing we should all be 'shocked' by is the number of so called geeks who dismiss genuine science/math with nothing more than vauge handwaves and ad-homs. I think it might be connected to a general lack of understanding of scientific skepticisim [wikipedia.org] or perhaps it's just plain old arrogance.

The novel finding in the paper is that they can use the properties of networks to automatically predict mis

Yes, the GP is not a troll, he's a karma whore. If Roland posts a science article, the whores will denounce it as fast as they can in expectation that they will get mode points, and those that actually read the article will support them later.
The sad thing is that 9 times out of 10 the article really will be crap. But I don't see any problem with this work. It's not revolutionary, but I wouldn't by any means call it bad science.

So, to the P&GP: enough with vague denunciations. If you have a problem

As is typical of a Roland the Plogger article, there's no link to the original article, but there's a link to his ad-laden blog.
Here's
the abstract [nanounion.net]:

Hierarchical structure and the prediction of missing links in networks
Nature 453, 98 (2008). doi:10.1038/nature06830
Authors: Aaron Clauset, Cristopher Moore
& M. E. J. Newman
Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks (protein interaction networks, metabolic networks or genetic regulatory networks) or communities in social networks. Here we present a general technique for inferring hierarchical structure from network data and show that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right-skewed degree distributions, high clustering coefficients and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partly known networks with high accuracy, and for more general network structures than competing techniques. Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.

So now, unlike Roland, we now have a clue what's being talked about. It's a scheme for finding some structure in networks and inferring what links might be missing.

Fair enough. Given that he provided copious links to the original articles in his blog post and that he provided a reasonably thorough summary (for a journalist) of the work, I can't say that this really bothers me at all. However, as is clear, I'm not overly familiar with Slashdot etiquette, so I'll just take your word for it that it's a Bad Thing.

Your argument boils down to "a tool can be used for good or evil", now since good and evil are subjective that reduces to "a tool can be used". Taken in the context of your post, this implies you are a Ludite but I don't think you are since a Ludite would not have the means to post on slashdot, ergo your post is irrational not the world.

"It's not hard to imagine how this might be misused [by the US administra

Clauset, Newman, and Moore are three of the most respected and well-known networks researchers. I'm not sure what you mean by being poorly presented or researched; care to elaborate?
But how this blog post makes the front page of/. is beyond me. It tells you absolutely NOTHING about the actual paper.

The meaning of the structure of networks is a stupid idea. The purpose maybe, the philosophy behind the structure maybe. But the meaning of?

In the context of the research (using known parts of a network's structure to predict unknown parts), I don't think the word "meaning" is out of place at all. A hierarchical clustering algorithm will extract some kind of hierarchy from any network you throw at it - but does that hierarchy mean anything? Does it contain information? This new research suggests that, for certain kinds of network, the extracted hierarchy is meaningful, because it allows us to make predictions about unknown parts of the network that we could not make without first extracting the hierarchy.

That's actually quite a profound discovery, because in the last ten years, complex networks (especially small-world and scale-free networks) have been held up as models of natural decentralisation and non-hierarchical self-organisation in many fields, from ecology to politics to communications to epidemiology. If such networks turn out to contain meaningful hierarchies (i.e. hierarchies that actually tell us something about how they function) then much of the rhetoric about complex systems will be turned on its head.

I'm not a mathematician, so maybe one can answer this question?
I know that I can take pretty much any open (e.g. not a ring) topology and document it in a hierarchical model.
Heck, if I'm permitted just a few multiple paths, I can model pretty much any topology with a such a drawing. Think org charts in any large corporation.
Abstraction is necessary for generalization, and models are absolutely necessary. Studying your model instead of your subject is a trap. The article presents a model for review;

Don't get me wrong I think it sounds like a great tool for abstracting complex systems into neat boxes for human consumption.I'm just imagining we'll have researchers wanting venture capital to create networks that can re-organise themselves simply by changing the hierarchy model used (perspective)The danger I see in this is people confusing the network in reality with a model of how we might perceive it, or perhaps of people being confused long enough to cough up the dough.